Search Results (19,977)

Purpose: Marking the one hundredth anniversary of quantum mechanics in 2025, quantum science has become foundational for the development of contemporary technologies, enabling advances in sensing, imaging, computing, and materials engineering. Cancer radiotherapy, although traditionally developed within the scope of classical dosimetric models and phenomenological biological frameworks, is fundamentally initiated by quantum-mechanical radiation-matter interactions. Radiation-induced DNA damage, which ultimately determines therapeutic effectiveness, originates from primary quantum-mechanical processes involving particle transport, electronic excitation and ionisation, followed by successive physicochemical and chemical stages including water radiolysis and radical formation. As scientific disciplines undergo a rapid “quantum transition,” radiation cancer treatment is increasingly positioned to benefit from deeper integration of quantum principles and emerging quantum technologies. Methods: This review examines how quantum mechanics governs the primary radiation-matter interactions that initiate the physical, physicochemical, chemical, and ultimately biological stages of radiation action at the (sub)cellular level, with particular emphasis on track structure, water radiolysis, DNA damage induction, and multiscale biological response. Contemporary approaches to DNA damage modeling are discussed, including track-structure Monte Carlo methods, nanodosimetric frameworks, and multi-scale simulation approaches that connect microscopic interaction events with biological outcomes. Key quantum concepts relevant to radiation therapy are outlined, together with emerging quantum technologies such as nanoscale quantum sensing, quantum lasers, quantum dots, and quantum computing, which are evaluated for their potential roles in dosimetry, imaging, treatment planning, and radiation transport simulations. In this context, artificial intelligence (AI) is considered a complementary tool to accelerate computation and integrate quantum-informed data across multiple scales. Results: The review highlights that quantum-informed modeling enables a more consistent description of radiation-induced processes across spatial and temporal scales, linking microscopic interaction mechanisms to DNA damage formation and macroscopic biological outcomes. Recent advances in track-structure and radiobiological modeling provide new opportunities for improving predictions of radiation effects and treatment response. Emerging quantum technologies show potential to enhance measurement sensitivity, improve simulation efficiency, and enable more precise control of radiation delivery. Furthermore, AI-assisted approaches facilitate the extraction of predictive patterns from complex datasets, supporting faster and more accurate estimation of biological endpoints such as DNA damage and cell survival. Conclusions: The quantum aspects of advanced treatment modalities, including proton and heavy-ion therapy, ultrafast radiation delivery, and the FLASH effect, as well as future concepts such as laser-plasma-driven and coherence-informed radiotherapy systems, indicate a promising direction for next-generation cancer treatment. By critically assessing both opportunities and limitations, this work provides a coherent framework for integrating DNA damage modeling, quantum principles, quantum-inspired techniques, emerging quantum technologies, and advanced computational tools to guide future developments in radiation oncology. Full article

Introduction: IgA nephropathy (IgAN) is a leading cause of end-stage renal disease. Given the significant adverse effects and inconsistent long-term efficacy of conventional immunosuppressive strategies, there is an unmet need for safer adjunctive therapies. Omega-3 polyunsaturated fatty acids (PUFAs) have been proposed as potential candidates to address this therapeutic gap. Purpose: This narrative review summarizes the proposed mechanisms of action of omega-3 PUFAs in IgAN and critically evaluates the current clinical evidence regarding their therapeutic potential and limitations. Mechanisms: Emerging experimental data suggest that omega-3 PUFAs may modulate inflammatory and fibrotic pathways relevant to kidney injury. Proposed mechanisms include modulation of eicosanoid metabolism, attenuation of NLR family pyrin domain-containing 3 (NLRP3) inflammasome activation, and generation of specialized pro-resolving mediators. In experimental studies, omega-3 PUFAs may also suppress nuclear factor kappa B (NF-κB)-driven transcription and attenuate mesangial cell proliferation, IgA immune-complex deposition, and transforming growth factor beta 1 (TGF-β1)/Smad3-mediated fibrotic signaling. Clinical Evidence: Conclusions: Omega-3 PUFAs have biological plausibility as adjunctive therapy in IgAN, but their clinical benefit remains uncertain. Available randomized trials and meta-analyses suggest possible modest effects on proteinuria in some settings, whereas evidence for preservation of kidney function or prevention of kidney failure is inconsistent and of low certainty. Future well-designed trials incorporating guideline-directed background therapy and biomarker-guided patient selection are essential to determine optimal dosing, formulation, biological exposure, and whether any patient subgroups derive clinically meaningful benefit. Full article

In light of the advancement of vehicle electrification and intelligence, four-wheel independent drive (4WID) electric vehicles (EVs) have garnered significant attention as a promising platform. Integrating advanced torque-vectoring (TV) strategies into 4WID EVs can effectively optimize the synergistic performance between handling stability and energy efficiency of the over-actuated system across various driving conditions. In this paper, a hierarchical Combined Sliding Mode Control–Adaptive Nonlinear Model Predictive Control (cSMC-ANMPC) TV strategy is proposed to enhance the comprehensive performance of 4WID EVs and ensure adaptive control across diverse driving conditions. Firstly, a hierarchical control architecture is developed to decouple the complex multi-objective problem. The upper layer performs robust stability decision-making by observing the vehicle’s state errors. The lower layer determines the optimal torque distribution throughout the powertrain. Secondly, a Combined Sliding Mode Controller (cSMC) is developed for the upper layer to promptly generate a robust stability command. By co-regulating both yaw rate and sideslip angle into a single command, it simplifies the lower layer’s task and enhances overall stability. Thirdly, a Soft Actor-Critic (SAC) intelligent tuner is integrated into the lower-layer NMPC to mitigate the effects of varying conditions on the stability–economy trade-off and strengthen the adaptability of the controller. Finally, co-simulation evaluations on the MATLAB R2023b/CarSim 2020.0platform demonstrate that the proposed cSMC-ANMPC strategy can improve comprehensive performance for the studied 4WID EV. Compared with other baselines, the stability enhancement in extreme maneuvers and the long-term energy-saving capability are remarkable, showcasing its promising performance. Full article

Atrial cardiomyopathy (ACM) is defined as any complex of structural, architectural, functional, electrophysiological, and molecular changes affecting the atria that may result in clinically significant health consequences. ACM can be caused by a variety of factors, including age-related changes, valvular or vascular disease, genetic diseases, congestive heart failure, metabolic diseases, cardiovascular disease (CVD) risk factors such as arterial hypertension (AH) or obesity, obstructive sleep apnea, and other infectious or noninfectious diseases predisposing to chronic inflammation. The diagnosis of ACM relies on several modalities, including electrocardiography, echocardiography, cardiac magnetic resonance imaging (MRI), computed tomography (CT), electroanatomical mapping (EAM), genetic studies, and biomarkers, which can detect and characterize structural, mechanical, and electrical atrial dysfunction. These changes often include structural atrial remodeling (fibrosis), abnormal structure of the atrial wall and its components, and contractile and electrical dysfunctions. When assessing aspects of ACM, structural changes in the atria such as left atrium (LA) size and fibrosis; LA architectural changes such as the expression of remodeling; changes in LA mechanics such as echocardiographic stress indices; changes in reservoir function and changes in contraction; biological factors determining changes in biomarkers; possible genetic predispositions and higher expression of certain genes encoding certain proteins; and arrhythmogenic factors associated with a higher risk of atrial fibrillation (AF) and stroke and a worse short- and long-term prognosis are very important. When considering the challenges of diagnosing ACM, it should be noted that without standardized diagnostics, most ACM diagnostic situations remain primarily research tools rather than practical clinical diagnostic methods. This review critically evaluates the evidence and translational gaps in the diagnosis of ACM, synthesizing the emerging role of advanced diagnostics and their clinical and prognostic implications as a key future tool for individual risk stratification. Full article

The integration of Artificial Intelligence (AI) into human resources management is driving a profound transformation in the evolution of management, and even more so in the management of human talent, which is the primary resource of any organization. This research provides an in-depth analysis of the impact of AI on core human resource management processes, covering the automation of operations that enables the exploration of dimensions such as talent acquisition, training, potential development, mental well-being, strategic workforce planning, job design, diversity, compensation, equity and inclusion, change management, culture and sustainability. The purpose of this study is to systematically synthesize the existing evidence on the impact of artificial intelligence on human management processes, identifying the scientific consensus, emerging contradictions, research gaps, and implications for sustainable organizational development. A systematic review was conducted of various sources published between 2020 and 2025 from databases such as ScienceDirect and Scopus, among others, using predefined Boolean search strategies, explicit inclusion and exclusion criteria and a structured thematic synthesis narrowing down the main studies based on search criteria. It was determined how algorithms are changing the employer-employee relationship within organizations. The findings indicate that the effectiveness of AI depends on the development of a hybrid intelligence that preserves the human factor consideration. It is concluded that AI enables the optimization of cultural change management, analytical precision, and ethical oversight—which are irreplaceable and critical human competencies in today’s digital age. This review contributes to the literature by providing a comprehensive synthesis of recent evidence, identifying unresolved research gaps, and proposing a future research agenda that will lead to the development of sustainable, responsible, and people-centered AI in human resource management. Full article

Exploring plant nutrient allocation and stoichiometry is critical to understanding the adaptive strategies of endangered trees in heterogeneous habitats. This study determined the concentrations of carbon (C), nitrogen (N), phosphorus (P), and potassium (K) in seven organs (leaves, current-year twigs, perennial branches, phloem, xylem, transport roots, and absorptive roots) of Parrotia subaequalis from eight wild populations in the Dabie Mountains and then analyzed the stoichiometric characteristics, chemical plasticity, and allometric relationships of these elements among organs. The concentrations of C, N, P, and K ranged from 416.31–513.61, 4.74–12.99, 0.74–12.89, and 2.62–10.34 mg g⁻¹, respectively. Belowground organs had significantly higher C:P, N:P, and N:K ratios than aboveground ones (by 72.83%–740.30%). Perennial organs (xylem, phloem, perennial branches) showed higher C concentrations and C:N, C:P, C:K, and P:K ratios but lower N, P, and K concentrations than current-year ones (leaves, current-year twigs). Xylem, phloem, and perennial branches exhibited the lowest coefficient of variation and plasticity index. N, P, and K exhibited isometric scaling (α = 0.96–1.04) between absorptive and transport roots. Leaf N and P were positively correlated with K (R² ≥ 0.53). Organ age is a critical determinant influencing the variation in stoichiometric characteristics of the organs. Overall, P. subaequalis adapts to nitrogen-limited wild habitats by adjusting N, P, and K nutrient up-take rates and allocation ratios across current-year organs. Full article

Background/Objectives: The proximity of the maxillary sinus floor to the tooth apices is critical for dental procedures, so preoperative evaluation is crucial for preventing complications. This study aims to examine the accuracy of panoramic radiographs in determining the relationship between the maxillary sinus and teeth, in comparison with cone-beam computed tomography (CBCT), which serves as the reference standard, and to develop a cluster-adjusted clinical prediction model to assist clinicians in identifying patients who may benefit from CBCT imaging. Methods: A total of 3436 teeth were evaluated using CBCT and panoramic radiographs for the relationship between root tips and sinuses. The McNemar–Bowker test and the R program were used to assess how accurate panoramic radiographs are by comparing them to CBCT imaging. The performance metrics of panoramic radiography were calculated, and the generalized estimating equation (GEE) logistic regression model and the interactive risk calculator were developed. Results: The McNemar–Bowker test indicated a statistically significant systematic difference between the two modalities (χ² = 190.01, p < 0.001); the overall three-category agreement was 82.2% (cluster-corrected 95% CI: 80.4–84.0%), corresponding to a dichotomized (protrusion vs. non-protrusion) accuracy of 87.6%. A cluster-corrected GEE logistic regression model, including panoramic classification, tooth type, age, and sex and developed and internally evaluated in this single-center cohort, predicted root protrusion identified using CBCT with high apparent discrimination (AUC = 0.948; cluster-based bootstrap 95% CI: 0.941–0.955, 1000 resamples; optimism-corrected AUC = 0.948) and satisfactory apparent calibration. Conclusions: This model, developed and evaluated internally within a single-center cohort, has the potential to serve as a clinical decision-support tool to help prioritize the need for CBCT in patients pre-evaluated with panoramic radiography. However, its performance reflects apparent, in-sample estimates, and external validation in independent populations is required before it should be considered for routine clinical use. Full article

Generative artificial intelligence (AI) is entering religious communication not only through chatbots and generated text, but also through images and reels on social media. Yet research in this field has paid limited attention to what images contribute beyond their accompanying words. This article examines how AI-assisted visuals are incorporated into Indonesian Muslim Instagram accounts linked to Nahdlatul Ulama (NU) and what communicative work they perform. Informed by the Religious Social Shaping of Technology framework and visual-culture scholarship, the study combines qualitative digital observation with content and multimodal analysis of eleven posts from three accounts and sampled comments. The visuals were adopted selectively within established organisational, humour-oriented, and preacher-led modes of address. AI functioned through amplification: synthetic construction gave bodily and spatial form to themes of national belonging, moral criticism, santri conduct, and interreligious engagement, while enabling encounters that could not readily be photographed. These moral positions became legible through familiar signs, but authority remained with the organisation, collective voice, or public persona through which the images were framed and circulated. AI-assisted imagery is therefore best understood as situated visual mediation: it expands what religious communicators can picture without independently determining the meaning or authority of what is pictured. Full article

This paper presents a resilience-driven probabilistic multiobjective framework for reactive power planning in islanded microgrids under extreme contingencies, explicitly integrating sustainability objectives and alignment with the United Nations Sustainable Development Goals (SDGs). The proposed planning framework simultaneously optimizes technical reliability, economic viability, environmental sustainability, and social resilience using the IEEE 33-bus distribution system as a representative test network. Uncertainties associated with solar irradiance, wind speed, and load demand are modeled using the Two-Point Estimation Method (2PEM), while the Non-dominated Sorting Genetic Algorithm II (NSGA-II) determines Pareto optimal planning solutions for five reactive power support strategies. The results demonstrate that planning solutions optimized for grid-connected operation are not necessarily the most effective under islanded conditions. Within the adopted multi-criteria evaluation framework, the dedicated D-STATCOM strategy achieves the highest overall normalized performance, providing 87.2% load preservation, 93.7% critical-load protection, and an 8.7 h representative survival time, while reducing total load shedding to 12.8% and eliminating high-risk shedding events (>30%). Furthermore, it decreases event-related economic losses by more than 75% and achieves the lowest environmental impact, with a 62.5% reduction in life-cycle CO₂ emission intensity relative to the conventional grid baseline. A normalization sensitivity analysis confirms that the comparative ranking of the investigated strategies remains unchanged under alternative normalization methods, demonstrating the robustness of the proposed evaluation framework. From a sustainability perspective, the proposed framework contributes to SDG 7 (Affordable and Clean Energy) through reliable low-carbon microgrid operation, SDG 9 (Industry, Innovation and Infrastructure) through resilient power system planning, SDG 11 (Sustainable Cities and Communities) by enhancing the continuity of critical urban services, SDG 13 (Climate Action) through reduced life-cycle emissions, and SDG 8 (Decent Work and Economic Growth) by supporting local employment associated with distributed energy deployment. Full article

The rapid evolution of Urban Air Mobility (UAM) necessitates high-performance morphing structures capable of seamless transitions between flight and ground modes. This research presents a rigorous structural optimization framework for a wing deployment mechanism, addressing the critical challenge of minimizing stress concentrations in cantilevered revolute joints. To overcome the computational prohibitive cost of traditional multibody dynamics, a Generalized Spectral Surrogate Model (GSSM) is introduced. This novel approach maps the mechanism’s geometric parameters to its kinetic response using polynomial-modulated Fourier series, reducing the evaluation time of ${10}^5$ design configurations from 4.2 h to merely 0.8 s while maintaining a determination coefficient $R^2>0.995$. Comparative analysis demonstrates that the GSSM outperforms Artificial Neural Networks and Kriging models in capturing periodic kinematic boundaries without spurious local minima. Through a weighted topological analysis, the study identifies a global optimum ($L_2=0.5$ m, ${\theta }_2=64.2^{\circ }$) that effectively shunts 70.3% of the aerodynamic load to the robust vehicle chassis. The proposed solution deviates from the theoretical unconstrained minimum by only 0.24%, providing a validated mathematical basis for the rapid synthesis of reliable aerospace mechanisms. Full article

Support load fluctuations in lithographic objectives can induce additional surface figure errors in lenses. Conventional Monte Carlo-based tolerance analysis is computationally expensive in high-dimensional load spaces, and surrogate models based only on raw load inputs often fail to accurately capture local-extremum responses such as peak-to-valley (PV). To address the load tolerance inversion problem under prescribed PV and root mean square (RMS) constraints, a tolerance inversion framework integrating Regional Peak-to-Valley Fluctuation Features, Active-Learning Gaussian Process Regression, and dual-metric tolerance boundary search (RPVF-ALGPR) is proposed. The framework transforms the local fluctuation information in low-order Zernike-reconstructed wavefronts into regional peak-to-valley fluctuation features and combines them with the original support loads as inputs to the GPR surrogate models. It further combines posterior-uncertainty-driven active learning to construct surrogate models for both metrics, thereby enabling the inverse determination of the critical load fluctuation boundary. One set of training results from a biconvex lens case study shows that the proposed method effectively improves PV and RMS prediction accuracy and reduces the number of samples required to reach the prescribed accuracy threshold by 35.7% compared with random sampling. The results provide a reference for support-load tolerance allocation and optomechanical stability evaluation of high-precision lenses. Full article

The corneal epithelium serves as the outermost transparent barrier of the eye and depends on rapid and coordinated cellular responses for wound repair. Although negative pressure (NP) has been shown to accelerate wound healing in other tissues, its cellular and molecular effects on corneal epithelium remain undefined. This study investigates how NP regulates corneal epithelial cell physiology and identifies the molecular mechanisms underlying NP-induced migration. Human corneal epithelial cells were exposed to normal or NP conditions, and cell motility was quantified using scratch-wound and transwell migration assays. Nuclear and cytoplasmic fractions were isolated for proteomic profiling to identify NP-responsive proteins. G3BP1 was selected as a candidate regulator and subsequently examined using molecular, biochemical, and functional assays to determine its role in NP-mediated signaling. Proteomic analysis revealed a significant NP-induced upregulation of G3BP1. Mechanistically, G3BP1 suppressed epithelial junctional proteins, including E-cadherin, p120-catenin, and ZO-1, while activating key pro-migratory signaling pathways involving AKT, ERK1/2, FAK, and Paxillin. These coordinated changes enhanced cytoskeletal dynamics and promoted corneal epithelial cell migration under NP stimulation. G3BP1 functions as a critical mechanotransduction mediator of NP, orchestrating adhesion remodeling and activating pro-migratory signaling cascades to facilitate corneal epithelial cell motility. These findings reveal a previously unrecognized cellular mechanism through which NP promotes epithelial repair and highlight G3BP1 as a potential therapeutic target for persistent corneal epithelial defects. Full article

Potato viral infections are among the main factors contributing to reduced quality of planting material and decreased tuber productivity. Currently, no reliable chemical control methods are available for plant viral diseases. Therefore, the development of potato cultivars carrying virus resistance genes remains one of the most effective and comprehensive approaches to this problem. In this study, 29 Russian and foreign potato cultivars, as well as 31 Far Eastern potato hybrids were evaluated. Resistance genes were identified using PCR analysis. The following cultivars carrying target resistance genes were used as positive controls for method calibration: Meteor (Rysto, Rx1, Sen1, Gpa2, H1), Vektor (Rx1, Gpa2), Yubilyar (Gpa2), and Zhukovsky ranniy (Gpa2, Rx1). Method calibration enabled determination of optimal magnesium chloride concentrations: 2.0 mM for Gpa2 and 2.5 mM for Rx1. Genotyping of 29 potato cultivars identified several highly resistant accessions, including Yubilyar, Zhukovsky ranniy, Bellarosa, Sante, Smak, Red Scarlett, and Laperla. These cultivars combined resistance to Potato virus X (Rx1) with complex resistance to two nematode species (Gpa2, H1). In the studied population, high frequencies of the Gpa2 (82.8%) gene and the H1 (65.5–69.0%) gene group were observed. Statistical analysis provided strong evidence for tight genetic linkage between the Rx1 and the Gpa2 loci on chromosome 12. The association was highly significant (p < 0.001). Analysis of 31 potato hybrids revealed 14 multi-marker genotypes with high breeding potential. A stable combination of five target resistance markers was consistently detected in their genomes. A dominant hybrid family derived from the Yantar × Smak cross was identified, represented by five related lines. For the first time, a precise heritability coefficient was calculated for the STS marker of the Rx1 gene in a Far Eastern hybrid population. The estimate reached h² = 0.835 at p = 0.01. This value significantly exceeded the critical threshold for breeding reliability (h² > 0.7), indicating largely additive genetic control of the trait. These results support targeted selection of parental combinations for breeding programs aimed at improving virus and nematode resistance in potato. Full article

This study investigates how institutional quality conditions the relationship between energy transition and environmental sustainability in nine CIS economies over the period 1996–2024, drawing on annual panel data sourced from the World Development Indicators. In the empirical framework, carbon dioxide emissions are specified as the dependent variable, while industrial output, foreign direct investment (FDI), renewable energy consumption, economic growth, trade openness, overall energy use, and an institutional quality index are included as key determinants of environmental pressure. Methodologically, the paper employs second-generation panel econometric techniques, commencing with cross-sectional dependence diagnostics and panel unit root tests, and proceeding to long-run estimation through FMOLS and CCR. The robustness of these estimates is reinforced using Driscoll-Kraay standard errors, while the System-GMM estimator is applied to address heteroskedasticity, serial correlation, cross-sectional dependence, and endogeneity concerns. The results indicate that industrial activity, energy consumption, and FDI significantly increase CO₂ emissions, whereas greater reliance on renewable energy and stronger institutional quality help to alleviate environmental degradation. Under more rigorous specifications, trade openness and economic growth are found to reduce emissions, pointing to emerging decoupling patterns within CIS countries. Importantly, the interaction between renewable energy and institutional quality reveals a pronounced complementary effect, suggesting that stronger governance frameworks amplify the environmental benefits of energy transition. Taken together, the findings underscore that environmental sustainability across CIS economies is jointly determined by structural, economic, and institutional factors, with institutional quality serving as a critical lever for advancing progress toward SDG 13. Full article

Umbilical cord mesenchymal stem cell-derived extracellular vesicles (UCMSC-EVs) hold strong promise for regenerative medicine, yet their intrinsic size heterogeneity remains a critical barrier to clinical translation, as it obscures molecular and functional specialization within bulk EV preparations. Here, we pioneer the application of the automated EXODUS platform to directly fractionate EVs from cell culture supernatants, resolving bulk UCMSC-EVs into three size-defined subpopulations. By integrating this platform with high-resolution mass spectrometry, we systematically characterize the molecular and functional landscapes of these UCMSC-EV size subpopulations. We demonstrate that EV size is tightly linked to distinct biogenetic origins, biomolecular corona composition, and functional programs: smaller EVs are enriched in exosome-associated proteins, ECM–glycan interfaces, and corona-associated molecules, and preferentially engage endocytosis- and phagosome-related pathways, whereas larger EVs exhibit ectosomal signatures. These findings identify EV size as a critical determinant of molecular architecture and biological function, providing insight into size-dependent EV heterogeneity and informing the rational design and optimization of UCMSC-EV-based therapeutic strategies. Full article